[ 1 ] Materials genome initiative for global competitiveness [EB/OL]‍.(2011-06-15)‍[2023-04-15]‍. https://www‍.‍mgi‍.‍gov/sites/default/files/documents/materials_genome_initiative-final‍.‍pdf#: ~: text=This%20Materials%20Genome%20Initiative%20for%20Global%20Competitiveness%20aims, materials%20in%20a%20more%20expeditious%20and%20economical%20way‍. 链接1

[ 2 ] Materials genome initiative strategic plan [EB/OL]‍. (2014-12-15)[2023-04-15]‍. https://www‍.nist‍.gov/system/files/documents/2018/06/26/mgi_strategic_plan_-_dec_2014‍.pdf#: ~: text=The%20Subcommittee%20on%20the%20Materials%20Genome%20Initiative%20%28SMGI%29, the%20goals%20of%20the%20Materials%20Genome%20Initiative%20%28MGI%29‍. 链接1

[ 3 ] Materials genome initiative strategic plan [EB/OL]‍. (2021-11-15)[2023-04-15]‍. https://www‍.mgi‍.gov/sites/default/files/documents/MGI-2021-Strategic-Plan‍.pdf‍. 链接1

[ 4 ] Horizon 2020: Details of the EU funding programme which ended in 2020 and links to further information [EB/OL]‍. [2023-04-15]‍. https: //research-and-innovation‍.ec‍.europa‍.eu/funding/funding-opportunities/funding-programmes-and-open-calls/horizon-2020_en#Article‍. 链接1

[ 5 ] Horizon Europe: Research and innovation funding programme until 2027 [EB/OL]‍. [2023-04-15]‍. https://research-and-innovation‍.ec‍.‍europa‍.‍eu/funding/funding-opportunities/funding-programmes-and-open-calls/horizon-europe_en‍. 链接1

[ 6 ] The future of manufacturing: A new era of opportunity and challenge for the UK [EB/OL]‍. [2023-04-15]‍. https://assets‍.publishing‍.service‍.gov‍.uk/government/uploads/system/uploads/attachment_data/file/255922/13-809-future-manufacturing-project-report‍.pdf‍. 链接1

[ 7 ] 谢建新 , 宿彦京 , 薛德桢 , 等‍ . 机器学习在材料研发中的应用 [J]‍. 金属学报 , 2021 , 57 11 : 1343 ‒ 1361 ‍.
Xie J X , Su Y J , Xue D Z , al e t ‍. Machine learning for materials research and development [J]‍. Acta Metallurgica Sinica , 2021 , 57 11 : 1343 ‒ 1361 ‍.

[ 8 ] Friederich P, Häse F, Proppe J, al et‍. Machine-learned potentials for next-generation matter simulations [J]‍. Nature Materials, 2021, 20(6): 750‒761‍.

[ 9 ] Srinivasan S, Batra R, Luo D, al et‍. Machine learning the metastable phase diagram of covalently bonded carbon [J]‍. Nature Communications, 2022, 13(1): 3251‍.

[10] Fish J, Wagner G J, Keten S‍. Mesoscopic and multiscale modelling in materials [J]‍. Nature Materials, 2021, 20(6): 774‒786‍.

[11] Yuan X Z, Zhou Y W, Peng Q, al et‍. Active learning to overcome exponential-wall problem for effective structure prediction of chemical-disordered materials [J]‍. NPJ Computational Materials, 2023, 9(1): 12‍.

[12] Park J H, Min K M, Kim H K, al et‍. Integrated computational materials engineering for advanced automotive technology: With focus on life cycle of automotive body structure [J]‍. Advanced Materials Technologies, 2022, 10: 2201057‍.

[13] Nikolaev P, Hooper D, Perea-Lopez N, al et‍. Discovery of wall-selective carbon nanotube growth conditions via automated experimentation [J]‍. ACS Nano, 2014, 8(10): 10214‒10222‍.

[14] Deneault J R, Chang J, Myung J, al et‍. Toward autonomous additive manufacturing: Bayesian optimization on a 3D printer [J]‍. MRS Bulletin, 2021, 46: 566‒575‍.‍

[15] Azoulay P, Graff-Zivin J, Uzzi B, al et‍. Toward a more scientific science [J]‍. Science, 2018, 361(6408): 1194‒1197‍.

[16] Burger B, Maffettone P M, Gusev V V, al et‍. A mobile robotic chemist [J]‍. Nature, 2020, 583(7815): 237‒241‍.

[17] Han G Q, Li G D, Huang J, al et‍. Two-photon-absorbing ruthenium complexes enable near infrared light-driven photocatalysis [J]‍. Nature Communications, 2022, 13(1): 2288‍.

[18] Tabor D P, Roch L M, Saikin S K, al et‍. Accelerating the discovery of materials for clean energy in the era of smart automation [J]‍. Nature Reviews Materials, 2018, 3(5): 5‒20‍.

[19] Kaufman J, Begley E‍. MatML: A data interchange markup language [J]‍. Advanced Materials and Processes, 2003, 161(11): 35‒37‍.

[20] Jain A, Ong S P, Hautier G, al et‍. Commentary: The materials project: A materials genome approach to accelerating materials innovation [J]‍. APL Materials, 2013, 1(1): 011002‍.

[21] Tshitoyan V, Dagdelen J, Weston L, al et‍. Unsupervised word embeddings capture latent knowledge from materials science literature [J]‍. Nature, 2019, 571(7763): 95‒98‍.

[22] Rao Z Y, Tung P Y, Xie R W, al et‍. Machine learning-enabled high-entropy alloy discovery [J]‍. Science, 2022, 378(6615): 78‒85‍.

[23] Xie T, C‍ Grossman J. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties [J]‍. Physical Review Letters, 2018, 120(14): 145301‍.

[24] Segler M H, Preuss M, P‍ Waller M. Planning chemical syntheses with deep neural networks and symbolic AI [J]‍. Nature, 2018, 555(7698): 604‒610‍.

[25] Lori A W, R‍ Gopal R. Frontiers of materials research: A decadal survey [J]‍. MRS Bulletin‍, 2017, 42(7): 537‍.

[26] Rapp K‍. Artificial intelligence in manufacturing: Real world success stories and lessons learned [EB/OL]‍. (2022-01-07)‍[2023-04-15]‍. https://www‍.‍nist‍.‍gov/blogs/manufacturing-innovation-blog/artificial-intelligence-manufacturing-real-world-success-stories‍. 链接1

[27] Flores-Leonar M M, Mejía-Mendoza L M, Aguilar-Granda A, al et‍. Materials acceleration platforms: On the way to autonomous experimentation [J]‍. Current Opinion in Green and Sustainable Chemistry, 2020, 25: 100370‍.

[28] Peterson E, Lavin A‍. Physical computing for materials acceleration platforms [J]‍. Matter, 2022, 5(11): 3586‒3596‍.

[29] Aspuru-Guzik A, Persson K‍. Materials acceleration platform: Accelerating advanced energy materials discovery by integrating high-throughput methods and artificial intelligence [EB/OL]‍. (2018-01-15)[2023-04-15]‍. https://dash‍.harvard‍.edu/handle/1/35164974?show=full‍. 链接1

[30] 宿彦京 , 付华栋 , 白洋 , 等‍ . 中国材料基因工程研究进展 [J]‍. 金属学报 , 2020 , 56 10 : 1313 ‒ 1323 ‍.
Su Y J , Fu H D , Bai Y , al e t ‍. Progress in materials genome engineering in China [J]‍. Acta Metallurgica Sinica , 2020 , 56 10 : 1313 ‒ 1323 ‍.

[31] Xie J X, Su Y J, Zhang D W, al‍‍ et. A vision of materials genome engineering in China [J]‍. Engineering, 2022, 10(3): 10‒12‍.

[32] Zhang H T, Fu H D, He X D, al et‍. Dramatically enhanced combination of ultimate tensile strength and electric conductivity of alloys via machine learning screening [J]‍. Acta Materialia, 2020, 200: 803‒810‍.

[33] Zhang H T, Fu H D, Zhu S C, al et‍. Machine learning assisted composition effective design for precipitation strengthened copper alloys [J]‍. Acta Materialia, 2021, 215: 117118‍.

[34] Wang C S, Fu H D, Jiang L, al et‍. A property-oriented design strategy for high performance copper alloys via machine learning [J]‍. NPJ Computational Materials, 2019, 5(1): 87‍.

[35] Wen C, Zhang Y, Wang C X, al et‍. Machine learning assisted design of high entropy alloys with desired property [J]‍. Acta Materialia, 2019, 170: 109‒117‍.

[36] Zhang Y, Wen C, Wang C X, al et‍. Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models [J]‍. Acta Materialia, 2020, 185: 528‒539‍.

[37] Wen C, Wang C, Zhang Y, al et‍. Modeling solid solution strengthening in high entropy alloys using machine learning [J]‍. Acta Materialia, 2021, 212: 116917‍.

[38] Liu P, Huang H, Jiang X, al et‍. Evolution analysis of γ´precipitate coarsening in Co-based superalloys using kinetic theory and machine learning [J]‍. Acta Materialia, 2022, 235: 118101‍.

[39] Wang W, Jiang X, Tian S, al et‍. Automated pipeline for superalloy data by text mining [J]‍. NPJ Computational Materials, 2022, 8(1): 9‍.

[40] Zhang T, Jiang Y, Song Z, al et‍. Catalogue of topological electronic materials [J]‍. Nature, 2019, 566(7745): 475‒479‍.

[41] Tang F, Po H C, Vishwanath A, al et‍. Comprehensive search for topological materials using symmetry indicators [J]‍. Nature, 2019, 566(7745): 486‒489‍.

[42] Yang Q, Yang S, Qiu P, al et‍. Flexible thermoelectrics based on ductile semiconductors [J]‍. Science, 2022, 377(6608): 854‒858‍.

[43] Li M X, Zhao S F, Lu Z, al et‍. High-temperature bulk metallic glasses developed by combinatorial methods [J]‍. Nature, 2019, 569(7754): 99‒103‍.